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Following solar flares (SF), the abrupt increase in X-radiation and EUV emission generates additional ionization and higher absorption of, e.g., electromagnetic waves in the sunlit hemisphere of the Earth’s ionosphere. The modeling of the ionosphere under solar flares are motivated by new observations with spacecrafts, satellites, and ground-based measurements. The estimation of modeling parameters for the ionospheric D-region during SF events poses a significant challenge, typically requiring a trial-and-error approach. This research presents a machine learning (ML) methodology for modeling the sharpness (β) and reflection height (H′) during SF events occurred from 2008 to 2017. The research methodology was divided into two separate approaches: an instance-based approach, which involved obtaining SF parameters during the peak SF, and a time-series approach, which involved analyzing time-series data during SFs. The findings of the study revealed that the model for the instance-based approach exhibited mean absolute percentage error (MAPE) values of 9.1% for the β parameter and 2.45% for the H′ parameter. The findings from the time-series approach indicated that the model exhibited lower error rates compared to the instance-based approach. However, it was observed that the model demonstrated an increase in β residuals as the predicted β increased, whereas the opposite trend was observed for the H′ parameter. The main goal of the research is to develop an easy-to-use method that provides ionospheric parameters utilizing ML, which can be refined with additional and novel data as well as other techniques for data pre-processing and other algorithms. The proposed method and the utilized workflow and datasets are available at GitHub.

The strongest X-class solar flare (SF) event in 24th solar cycle, X9.3, occurred on 6 September 2017, accompanied by earthward-directed coronal mass ejections (CMEs). Such space weather episodes are known to cause various threats to human activities ranging from radio communication and navigation disturbances including wave blackout to producing geomagnetic storms of different intensities. In this study, SFs’ ionospheric impacts and effects of accompanied heliospheric disturbances on primary cosmic rays (CR) are investigated. This work offers the first detailed investigation of characteristics of these extreme events since they were inspected both from the perspective of their electromagnetic nature, through very low frequency (VLF) radio waves, and their corpuscular nature of CR by multi-instrumental approach. Aside data recorded by Belgrade VLF and CR stations, data from GOES and SOHO space probes were used for modeling and analysis. Conducted numerical simulations revealed a significant change of ionospheric parameters (sharpness and effective reflection height) and few orders of magnitude increase of electron density. We compared our findings with those existing in the literature regarding the ionospheric response and corresponding parameters. In addition, Forbush decrease (FD) magnitude, corrected for magnetospheric effect, derived from measurements, and one predicted from power exponents used to parametrize the shape of energetic proton fluence spectra at L1 were compared and found to be in good agreement. Presented findings could be useful for investigation of atmospheric plasma properties, particles’ modeling, and prediction of extreme weather impacts on human activities.